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| X-Ray Fluorescence Spectroscopy Combined With Discriminant Analysis to Identify Imported Iron Ore Origin and Brand |
| ZHANG Bo1，2, MIN Hong2, LIU Shu2*, AN Ya-rui1*, LI Chen2, ZHU Zhi-xiu2 |
1. Department of Chemistry，College of Science，University of Shanghai for Science and Technology，Shanghai 200093，China
2. Technical Center for Industrial Product and Raw Material Inspection and Testing，Shanghai Entry-Exit Inspection and Quarantine Bureau，Shanghai 200135，China |
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Abstract Iron ore is an important raw material for the iron and steel industry. Imported iron ore with different origins and brands varies in elemental composition and content. Phenomena such as doping, adulteration and shoddy of imported iron ore are endangering the national security and economy safety, so it is necessary to establish a rapid identification model of the origin and brand of imported iron ore in major importing countries, can support the risk supervision of imported iron ore, and ensure trade facilitation. The research objects of this paper are 236 imported iron ore samples from 14 brands in Australia, South Africa and Brazil, including Pilbara Blend Fines (Lumps), Yandi Fines, Newman Blend Fines(Lumps), Jimblebar Blend Fins, Kings Fines, Fortescue Blend Fines, Kumba Standard Fines (Lumps), and Carajas Iron Ore, etc. The elemental composition and content of all research samples were determined by wavelength dispersive X-ray fluorescence spectrum standard-less analysis method, and it turned out that elements detected from iron ore samples are 24 in total, including Fe, O, Si, Ca, Al, Mn, Tb, Ti, Mg, P, K, S, Cr, Na, Sr, Zr, Zn, V, Cu, Gd, Ba, Cl, Ni, and Co. Among them, we chose 12 elements and conducted a stepwise discriminant-Fisher discriminant analysis modeling, including Fe, O, Si, Ca, Al, Mn, Tb, Ti, Mg, P, Cr, and S. Moreover, 10 elements including Fe, O, Si, Ca, Al, Mn, Ti, Mg, P, S were screened out as valid variables by the stepwise discriminant method. A two-dimensional Fisher discriminant model was thus established to realize the identification of imported iron ore from Australia, South Africa and Brazil. The recognition accuracy of the model for the modeled sample was 97.40%, the one of cross-validation was 95.30%, and that of the test sample reached 95.50%. For the 14 brands of iron ore, 10 elements including Fe, O, Si, Ca, Al, Mn, Ti, Mg, P, and S were used to establish a ten-dimensional Fisher discriminant model, and its recognition accuracy for the modeled sample was 100%. The accuracy of cross-validation was 97.90%, while one of the test samples reached 100%. Although wavelength dispersion X-ray fluorescence spectrum standard-less analysis method is a semi-quantitative analysis method, the analysis is fast and stable, wavelength dispersive X-ray fluorescence spectrum standard-less analysis method together with the stepwise discriminant-Fisher discriminant analysis can realize the identification of importing countries and brands of iron ore.
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Received: 2019-07-01
Accepted: 2019-11-10
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Corresponding Authors:
LIU Shu, AN Ya-rui
E-mail: liu_shu@customs.gov.cn; anyarui@usst.edu.cn
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[1] Clegg S M，Sklute E，Dyar M D，et al. Spectrochimica Acta Part B: Atomic Spectroscopy，2009，64(1)：79.
[2] Sheng L W，Zhang T L，Niu G H，et al. J. Anal. At. Spectrom.，2015，30(2)：453.
[3] Yan C H，Wang Z M，Ruan F，et al. Anal. Methods，2016，8(32)：6216.
[4] MENG Hai-dong，YIN Yue，SUN Jia-ju，et al（孟海东，殷 跃，孙家驹，等）. West-China Exploration Engineering（西部探矿工程），2012，24(8)：137.
[5] Khajehzadeh N，Haavisto O，Koresaar L. Minerals Engineering，2017，113：83.
[6] WU Su-ru，GU Song-hai，SONG Yi，et al（武素茹，谷松海，宋 义，等）. Computers and Applied Chemistry（计算机与应用化学），2014，31(12)：1543.
[7] YAO Chun-yan，YAO Zhong-you，XU Ming，et al（姚春彦，姚仲友，徐 鸣，等）. Geological Bulletin of China（地质通报），2014，33(2-3)：215.
[8] Sabzi S，Javadikia P，Rabani H，et al. Measurement，2013，46(9)：3333.
[9] Granato D，Putnik P，Kovačevic′ D B，et al. Comprehensive Reviews in Food Science and Food Safety，2018，17(3)：663.
[10] CAO Xiao-lan，CHEN Xing-ming，ZHANG Shuai，et al（曹晓兰，陈星明，张 帅，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2018，38(5)：1547.
[11] Luo R J，Jiang T，Chen X B，et al. Food Chemistry，2019，274：1. |
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